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Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria.
Semantics of probabilistic programs
Kozen, D · 1979
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Practical markov chain monte carlo
Geyer, C. J · 1992
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Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics
Evensen, G · 1994
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Monte Carlo Methods in Finance
Jäckel, P · 2002
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Towards a comprehensive simulation model of malaria epidemiology and control
Smith, T., Maire, N., Ross, A., Penny, M., Chitnis, N., Schapira, A., Studer, A., Genton, B., Lengeler, C., Tediosi, F., et al · 2008
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The boost C++ libraries
Schäling, B · 2011
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Epidemiology of malaria in endemic areas
Autino, B., Noris, A., Russo, R., and Castelli, F · 2012
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Probabilistic programming
Gordon, A. D., Henzinger, T. A., Nori, A. V., and Rajamani, S. K · 2014
Cited alongside, same era.
A Guide to Monte Carlo Simulations in Statistical Physics
Landau, D. P. and Binder, K · 2014
Cited alongside, same era.
Black box variational inference
Ranganath, R., Gerrish, S., and Blei, D · 2014
Cited alongside, same era.
Defining the relationship between infection prevalence and clinical incidence of plasmodium falciparum malaria
Cameron, E., Battle, K. E., Bhatt, S., Weiss, D. J., Bisanzio, D., Mappin, B., Dalrymple, U., Hay, S. I., Smith, D. L., Griffin, J. T., et al · 2015
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Global technical strategy for malaria 2016-2030, 2015
WHO · 2015
Cited alongside, same era.
Inference compilation and universal probabilistic programming
Inference compilation and universal probabilistic programming
Le, T. A., Baydin, A. G., and Wood, F · 2017
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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The 2030 agenda for sustainable development
UN, U. N · 2017
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Efficient probabilistic inference in the quest for physics beyond the standard model
Baydin, A. G., Heinrich, L., Bhimji, W., Gram-Hansen, B., Louppe, G., Shao, L., Cranmer, K., Wood, F., et al · 2018
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Implementation and applications of emod, an individual-based multi-disease modeling platform
Bershteyn, A., Gerardin, J., Bridenbecker, D., Lorton, C. W., Bloedow, J., Baker, R. S., Chabot-Couture, G., Chen, Y., Fischle, T., Frey, K., et al · 2018
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Le, T. A., Baydin, A. G., and Wood, F · 2016
Cited alongside, same era.
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
Staton, S., Yang, H., Wood, F., Heunen, C., and Kammar, O · 2016
Cited alongside, same era.
OpenMalaria and EMOD: A case study on model alignment
Ferris, C., Raybaud, B., and Madey, G
Cited in the paper.
Challenges to the implementation of malaria policies in malawi
Mwendera, C. A., de Jager, C., Longwe, H., Kumwenda, S., Hongoro, C., Phiri, K., and Mutero, C. M
Cited in the paper.
Ensemble modeling of the likely public health impact of a pre-erythrocytic malaria vaccine
Smith, T., Ross, A., Maire, N., Chitnis, N., Studer, A., Hardy, D., Brooks, A., Penny, M., and Tanner, M
Cited in the paper.
The sustainable development goals report 2018
UN · 2018
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Pyro: Deep universal probabilistic programming
Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip, P., Horsfall, P., and Goodman, N. D · 2019
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